We investigate how humans discover hidden dependencies among variables in the visual environment over time. We first perform a visuo-motor experiment to establish that it is possible for humans to learn hidden models of varying complexity over time. Participants perform an experiment in which there is a hidden relationship between the value of an observed variable (location of a visual cue) and the required value of the response variable (interception time to a target). This relationship between the location and the time of a target represents models of different complexities (Constant, Linear, Quadratic) that suddenly switch over the course of the experiment. Given the data, we infer which model was being used to generate the responses at different stages of the experiment and simultaneously control for the different number of parameters in each model. We use Bayesian model selection to determine the posterior probability of each model given the data. We find that participants were able to correctly detect whether the hidden relationship in the stimuli followed a constant, linear or quadratic model. When the model that was used to generate the stimuli changed, participants were able to follow the change. In summary, participants constantly monitored the world relationship between the location and the time of a visual event and exhibited a preference for the simplest model that adequately explained the observed data.